AIMC Topic: Signal Processing, Computer-Assisted
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Machine learning based analysis and detection of trend outliers for electromyographic neuromuscular monitoring.
Journal of clinical monitoring and computing
Apr 4, 2024
PURPOSE: Neuromuscular monitoring is frequently plagued by artefacts, which along with the frequent unawareness of the principles of this subtype of monitoring by many clinicians, tends to lead to a cynical attitute by clinicians towards these monito...
Harnessing machine learning for EEG signal analysis: Innovations in depth of anaesthesia assessment.
Artificial intelligence in medicine
Apr 4, 2024
Anaesthesia, crucial to surgical practice, is undergoing renewed scrutiny due to the integration of artificial intelligence in its medical use. The precise control over the temporary loss of consciousness is vital to ensure safe, pain-free procedures...
Automated cardiac arrhythmia detection techniques: a comprehensive review for prospective approach.
Computer methods in biomechanics and biomedical engineering
Apr 2, 2024
Abnormal cardiac functionality produces irregular heart rhythms which are commonly known as arrhythmias. In some conditions, arrhythmias are treated as very dangerous which may lead to sudden cardiac arrest. The incidence and prevalence of cardiac an...
Applying Common Spatial Pattern and Convolutional Neural Network to Classify Movements via EEG Signals.
Clinical EEG and neuroscience
Mar 24, 2024
Developing an electroencephalography (EEG)-based brain-computer interface (BCI) system is crucial to enhancing the control of external prostheses by accurately distinguishing various movements through brain signals. This innovation can provide comfor...
Using multivariate pattern analysis to increase effect sizes for event-related potential analyses.
Psychophysiology
Mar 22, 2024
Multivariate pattern analysis (MVPA) approaches can be applied to the topographic distribution of event-related potential (ERP) signals to "decode" subtly different stimulus classes, such as different faces or different orientations. These approaches...
A hybrid EEG classification model using layered cascade deep learning architecture.
Medical & biological engineering & computing
Mar 20, 2024
The problem of multi-class classification is always a challenge in the field of EEG (electroencephalogram)-based seizure detection. The traditional studies focus on computing or learning a set of features from EEG to distinguish between different pat...
Application of Convolutional Neural Network for Decoding of 12-Lead Electrocardiogram from a Frequency-Modulated Audio Stream (Sonified ECG).
Sensors (Basel, Switzerland)
Mar 15, 2024
Research of novel biosignal modalities with application to remote patient monitoring is a subject of state-of-the-art developments. This study is focused on sonified ECG modality, which can be transmitted as an acoustic wave and received by GSM (Glob...
Feasibility and validity of using deep learning to reconstruct 12-lead ECG from three‑lead signals.
Journal of electrocardiology
Mar 8, 2024
BACKGROUND: In the field of mobile health, portable dynamic electrocardiogram (ECG) monitoring devices often have a limited number of lead electrodes due to considerations, such as portability and battery life. This situation leads to a contradiction...
Fast reconstruction of EEG signal compression sensing based on deep learning.
Scientific reports
Mar 1, 2024
When traditional EEG signals are collected based on the Nyquist theorem, long-time recordings of EEG signals will produce a large amount of data. At the same time, limited bandwidth, end-to-end delay, and memory space will bring great pressure on the...
12-Lead ECG Reconstruction Based on Data From the First Limb Lead.
Cardiovascular engineering and technology
Feb 29, 2024
PURPOSE: Electrocardiogram (ECG) data obtained from 12 leads are the most common and informative source for analyzing the cardiovascular system's (CVS) condition in medical practice. However, the large number of electrodes, specific placements on the...
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